variables or which ones to use. The results of a recursive forecasting exercise reveal a statistically significant increase in …

. Forecasting from such a model assuming "no structural break" and "correct model" is tantamount to ignoring important aspects of …) a random walk model. Optimal IC approach, though computational intensive, outperforms in forecasting next period …

as benchmark. Finally, we replicate the forecasting experiment including as predictors both an indicator of unemployment …

forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … forecasting model that could guarantee a sound policy decisions. …

improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques …-horizon Superior Predictive Ability (uSPA) tests, used to select the best forecasting model by combining different horizons. Our sample … forecasting horizons that are more than one month ahead using the mean square error, and the aggregated ETS has better forecasting …

, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting …

We test and report on time series modelling and forecasting using several US. Leading economic indicators (LEI) as an … input to forecasting real US. GDP and the unemployment rate. These time series have been addressed before, but our results … unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period …

volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate …